Cerebral Cortex Advance Access published June 5, 2015
Cerebral Cortex, 2015, 1–9
doi: 10.1093/cercor/bhv120
Original Article
ORIGINAL ARTICLE
Purely Translational Realignment in Grid Cell Firing
Patterns Following Nonmetric Context Change
Elizabeth Marozzi1,†, Lin Lin Ginzberg1,2,†, Andrea Alenda3, and Kate J. Jeffery1
1
Address correspondence to Dr Kate Jeffery, Department of Experimental Psychology, Division of Psychology and Language Sciences,
University College London, London, UK. Email: k.jeffery@ucl.ac.uk
†
Elizabeth Marozzi and Lin Lin Ginzberg contributed equally to this work.
Abstract
Grid cells in entorhinal and parahippocampal cortices contribute to a network, centered on the hippocampal place cell system,
that constructs a representation of spatial context for use in navigation and memory. In doing so, they use metric cues such
as the distance and direction of nearby boundaries to position and orient their firing field arrays (grids). The present study
investigated whether they also use purely nonmetric “context” information such as color and odor of the environment.
We found that, indeed, purely nonmetric cues—sufficiently salient to cause changes in place cell firing patterns—can regulate
grid positioning; they do so independently of orientation, and thus interact with linear but not directional spatial inputs.
Grid cells responded homogeneously to context changes. We suggest that the grid and place cell networks receive context
information directly and also from each other; the information is used by place cells to compute the final decision of the spatial
system about which context the animal is in, and by grid cells to help inform the system about where the animal is within it.
Key words: context, grid cells, place cells, sensory integration, spatial learning
Introduction
The entorhinal–hippocampal network forms the core of a spatial
memory system that supports cognitive processes such as navigation and episodic memory. While hippocampal place cells
exhibit focal, sparse, and irregular activity patches (firing fields)
on an open-field arena, entorhinal grid cells show spatially regular firing field arrays (Hafting et al. 2005). Both place and grid cells
position their firing fields with the aid of spatial cues such as local
environmental boundaries. However, place cells also respond to
nonmetric information (Wood et al. 2000; Anderson and Jeffery
2003; Leutgeb et al. 2005), and responses to metric and nonmetric
inputs can be manipulated independently (Jeffery and Anderson
2003); these so-called contextual inputs are thus segregated until
quite late in the processing pathway. The aim of the present
experiment was to determine whether grid cells also respond to
nonmetric contextual cues, and thereby gain insights into
where and how the convergence of metric and nonmetric signals
occurs.
A previous study of grid cells found that, while metric environmental change induced translation and sometimes rotation of
grids, nonmetric change (to box color) caused no shift of grids
(Fyhn et al. 2007), suggesting insensitivity to nonmetric inputs.
However, those contextual changes did not induce “global remapping” (Leutgeb and Leutgeb 2007) of hippocampal place cells, and
so may not have been salient enough to trigger responding by the
grid cell system. In cases where grids changed their relationships
© The Author 2015. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/),
which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
1
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Department of Experimental Psychology, University College London, London WC1H 0AP, UK,
Department of Cell and Developmental Biology, University College London, London WC1E 6BT, UK
and 3Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK
2
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Materials and Methods
Subjects
Eighteen male Lister Hooded rats (Charles River, UK; 300–350 g)
were implanted with tetrodes aimed at the medial entorhinal
cortex alone (MEC; n = 13) or both MEC and hippocampal CA1
(n = 5, the hippocampal data are not reported here). Fourteen
rats were recorded in small context boxes (see below) and 7 in
large, with 3 animals recorded in both. After implantation, animals were housed singly and their food restricted to 90% of
their free-feeding weight. Experiments were conducted according to the UK Animals (Scientific Procedures) Act 1986.
Figure 1. Hypotheses concerning relationships between context inputs, grid cells,
and place cells. (a) The context signal is routed through the entorhinal grid cells
and acts by regulating the position/orientation of the grids, which in turn regulate
where place fields are positioned via a feedforward mechanism. (b) Grid cells are
insensitive to context and the context signal acts directly on place cells ( perhaps
Apparatus
For the small-box trials, the apparatus used was the same as that
used previously in Anderson and Jeffery (2003). This comprised 2
transparent acrylic boxes 60 × 60 cm square with walls 50 cm
high (Fig. 2), each wiped repeatedly throughout the experiment
with either lemon or vanilla food flavoring. These inserts could
then be placed into one of two slightly larger wooden boxes,
one painted black and the other white. This allowed the apparent
color of the boxes to change, creating 4 compound contexts:
black-lemon, black-vanilla, white-lemon, and white-vanilla. For
the large-box trials, the boxes (also acrylic) were 120 × 120 cm
square with walls 50 cm high. Because these enclosures were
too large to allow wooden casings to be manipulated, color
changes were induced using 4 wooden panels painted white or
black which were placed against each wall of the box, with a
black or white sheet used to make the color of the floor.
Before each trial, the inner surface of the floor of the box was
cleaned with tissues dampened with a small amount of ethanol
by gating the entorhinal input). (c) The context signal acts directly on place cells
which feed the computed output back to the grid cell network, thus positioning
the grids; grid cells are thus context sensitive, but are the consequence rather
than cause of place cell context sensitivity.
and scented with lemon or vanilla food flavoring (Supercook, UK)
by placing the flavoring (0.5 mL for the small boxes, 2.0 mL for the
large) onto a clean paper towel and wiping the floor of the box as
uniformly as possible. Context boxes were always placed in exactly
the same position within the laboratory room, with all distal cues in
the testing room (such as recording equipment and shelves) remaining in the same positions and available for the rat to see at
all times. The box not in use during a trial was placed against the
wall of the experimental room out of view of the rat.
Electrode Implantation
Unit recordings were made using bundles of 4 tetrodes loaded
into an Axona microdrive (Axona Ltd, St Albans, UK), using
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to the local boundaries, spatial changes to the environment had
also occurred (e.g., shift of the apparatus to a new room, or induction of darkness). It thus remains possible that grid cells would be
entirely insensitive to purely nonmetric changes to the environment. To investigate this issue, we employed a nonmetric context-change paradigm known to induce place cell remapping
(Anderson and Jeffery 2003), and measured changes in grid cell
firing. We wanted to know (a) whether grid cells would “remap”
(realign) to nonmetric change, and (b) if so, whether they would
do so coherently (all in the same way) or individually, and (c)
whether they could respond to unique configurations of color
and odor, in the way that place cells do.
At the outset, we had 3 hypotheses about the possible relationship between grid cells, place cells, and context (Fig. 1). The first is
that grid cells may be fully responsive to context changes even in
the case where no metric changes were made to the environment,
and these responses may drive place cell remapping (supported by
Fyhn et al. (2007)). Since place cell remapping in these conditions is
“partial” (not all cells respond to every change; Anderson and Jeffery 2003), it is of interest to observe whether grid cell firing might
also be partial, although this seems a priori unlikely given the documented attractor dynamics of the system (Yoon et al. 2013). The second hypothesis is that grid cells may be entirely insensitive to
context, with place cells receiving context information directly
from some other source, which could act by gating the entorhinal
feedforward projections (Hayman and Jeffery 2008). And finally, it
may be that place cells receive context information directly, and
feed this back to the grid cells; a hypothesis supported by
developmental work (Langston et al. 2010; Wills et al. 2010) and inactivation studies (Brandon et al. 2011, 2014; Bonnevie et al. 2013).
Grid Cell Realignment Following Nonmetric Context Change
Marozzi et al.
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WL = white-lemon, WV = white-vanilla. Red numbers to show trial order, black numbers show firing rate in Hz. (b) Pairwise correlations of the trials in (a) showing how the
typical remapping patterns seen in the ratemaps corresponded to a numerical correlation value. Inset shows the small white context box. The correlation matrix shows
the ratemap correlation values collected for 2 blocks of 4 trials. Each trial type has been compared with each of the 2 trials of the other types, as well as with its counterpart
in the other block (shown in red). Cases where clear remapping was evident visually (e.g., black-vanilla and white-lemon) generated low correlation values. (c) Grid
ratemap correlations for same-context and different-context comparisons, showing preponderance of low correlations (realignment) in the latter. Dotted line shows
the categorical realignment threshold. (d) Correlations for the different ratemap comparisons, including the within-trial comparisons between first and second trialhalves. Sensitivity (decreased correlation) was higher to odor change than color change.
methods described previously (Barry et al. 2007). Recordings were
made from unilateral posterior cortical sites (coordinates from
lambda: 1.2 mm AP, 4–4.5 mm medio-lateral; angled 0–8° from
vertical; n = 14).
Recording Procedure
Animals were brought into the recording room individually in a
covered carrying box, and were then removed, connected to the
recording equipment (DacqUSB, Axona Ltd, St Albans, UK) via a
headstage and 3-m fine cable, and placed on a holding platform.
Extracellular potentials were recorded from each of the electrodes and the signal was amplified (8000–38 000 times) and bandpass filtered (500 Hz to 7 kHz). Each channel was sampled at
50 kHz and action potentials were stored at 50 points per channel
whenever the signal exceeded a user-defined threshold (0.2 ms
prethreshold and 0.8 ms post-threshold, total 1 ms). Each of the
four wires of one tetrode was referenced to the signal from a
wire on another tetrode of the same microdrive. The headstage
carried 1 or 2 different-sized light-emitting diodes, the positions
of which were recorded via an overhead camera to monitor
position and head direction. Spike events, local field potentials,
and positional information were recorded and stored for offline
analysis.
If a putative grid cell was found during a screening session
(usually conducted on a larger arena in a different room), then
the animal was moved into the experimental room, connected
to recording equipment, and placed on a holding platform,
where it rested in between recording trials. It was then subjected
to the experimental protocol, comprising a sequence of foraging
trials in different configurations of the contexts. During each
trial, rice was scattered randomly into the environment to ensure
even spatial sampling, while spike and position data were collected. Each recording trial lasted 10 min (for the small-box trials)
or 15 min (for the large-box trials). Between trials, animals were
returned to the holding platform for a few minutes while the apparatus was reconfigured. The order of the trials was varied
throughout the experiment, such that every context was experienced at least once, and same-context trials were never consecutive. For 13 rats, the series of 4 contexts was followed by a single
repeated trial of one of the conditions randomly selected; for the
other 5 rats, the entire sequence of 4 conditions was repeated, in a
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Figure 2. Grid cell realignment following nonmetric context change. (a) Top: L-R, a spike plot showing the rat’s path in black and the cell’s action potentials as red dots;
same data plotted as a ratemap; autocorrelogram. Bottom: firing pattern of the same cell in the 4 small contexts (each run twice). BL = black-lemon, BV = black-vanilla,
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different order. Animals that were run in both the small and large
context box experiments were run in each experiment in a
pseudorandom fashion with at least 4 h between the recordings.
separation point at 0.35 (Fig. 2b) and so this value was used as the
threshold for categorical comparison of realigning versus nonrealigning cell numbers.
Data Analysis
Extracting Grid Metrics
Spike data were analyzed offline using a cluster-cutting program
(Tint; Axona Ltd, St Albans, UK) followed by analysis in Matlab
(The MathWorks, Natick, MA, USA). Units were isolated manually
or with the help of an automated clustering algorithm (KlustaKwik; Kadir et al. 2014). Spatial firing was assessed by plotting the
spikes on the path of the rat; cells that appeared to show spatial
firing visually were then subjected to a series of analyses to determine whether they would be included in the final dataset. Cells
were accepted into further analysis if at least one of their
between-trial ratemap correlations (see below) was >0.49. Cells
were considered grid cells if they were 1) recorded on MEC-targeted electrodes, and 2) if at least one of their grid scores, in
screening or context trials, was >0. We treated the data from
the small- and large-box trials separately; within these datasets,
cells were considered to be repeat instances, and hence discarded, if they 1) occurred on the same tetrode, 2) had similar waveforms (i.e., their maxima occurred on the same electrode), and
3) were recorded within 10 days of the previous recording in the
same box type. Where more than one grid cell cluster occurred on
the same tetrode, we checked that these were unlikely to be from
the same cell by first correlating their 2 ratemaps on a blackvanilla trial, and then, if the correlation exceeded 0.3 (which
was the case for 6 cell pairs), checking that the clusters were
nevertheless well separated in the cluster plot.
Spatial autocorrelograms of the smoothed ratemaps were generated and used for assessment of the scale and orientation of grids
(Hafting et al. 2005; Sargolini et al. 2006; Barry et al. 2007). The
cross-correlogram was generated in the same way as the autocorrelograms, except that a given map was correlated against
another instead of itself.
Statistical Analysis
Grid Scale
Firing characteristics were analyzed as described below. Parametric comparisons of these between conditions used t-tests, analysis
of variance (ANOVA) (with post hoc tests Bonferroni corrected) and
χ 2 analyses.
Locational firing rate maps were made by binning data in 2 × 2 cm
bins, dividing by dwell time, and smoothing with a 5-bin boxcar
filter.
Grid scale was obtained from the autocorrelograms generated
from the ratemaps in the large context boxes, by taking, for each
trial, the mean of the distance (in bins) of the 6 closest peaks to
the central peak. The difference in grid scale between 2 trials
was expressed as a difference score, calculated as the difference
between the scales divided by their sum. Scores were computed
for trial pairs of the same condition, trial pairs of different conditions for the same cell, and trial pairs between randomly selected
cells obtained when the entire dataset was shuffled; these were
compared using one-way ANOVA.
Firing Rate Analysis
Grid Orientation
The peak firing rate for a trial (in Hz) was the firing rate in the
highest bin. To assess changes in firing rate, a rate difference
score between 2 recording trials was calculated (Leutgeb et al.
2005), being the absolute difference between the rates divided
by their sum.
Grid orientation was calculated from the autocorrelograms.
Orientation was defined as the angle between the x-axis and a
line connecting the center of the autocorrelogram to the center
of the nearest peak encountered anticlockwise from the positive
x-axis. Average change in orientation for a given set of 5 trials was
derived by calculating the absolute (unsigned) change in orientation between each trial pair and averaging. These values were
compared against trial-pair differences for the repeated trials
(in which orientation differences should be small) and for the
same grid set shuffled (in which differences should be larger, although not as large as those chosen from a random distribution
due to additional environmental influences on grid orientation
(Stensola et al. 2012)).
Spatial Correlations
Spatial correlations between pairs of ratemaps were computed by
calculating a Pearson’s r correlation between firing rates of homologous pixels in the 2 smoothed maps. Unvisited bins as well as
bins having zero firing in both maps were excluded from this calculation in order to prevent artificially high correlations. Grid
cells were only entered into the final analysis if at least one of
their trial-pair correlations in the trial series was >0.49, this
value being the 95th percentile of a distribution derived from correlating ratemaps chosen randomly from pairs of co-recorded
cells. A frequency histogram comparing the distributions of
same-trial versus different-trial correlations revealed a maximal
Periodic grid firing fields (Sargolini et al. 2006) were calculated as
follows: a circular region was defined, the radius of which was the
furthest distance from the center of the spatial autocorrelogram
to the center of one of the six peaks. This circular region was then
extracted and a copy rotated in 30° increments up to 180° (5 rotations) and re-correlated each time with the original. Due to the
hexagonal periodic firing of grid cells, the correlations between
the original and rotated copy vary sinusoidally with highest correlations occurring at rotations of 60°, 120°, or 180° and lowest at
rotations of 30°, 90°, or 150°. The difference between the highest
and lowest correlation peak comprised the grid score. The grid
score was calculated from the large-arena screening trials
where these were available (n = 79 cells) or from the recording
trials if screening trials were unavailable (n = 21), and cells were
rejected (n = 3) if none of their scores exceeded 0.
Phase Shift
The amount the fields of a grid were shifted relative to the box
walls following a context change was calculated from the crosscorrelogram (Hafting et al. 2005) of the ratemaps of the 2 trials
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Ratemap Generation
Grid Score
Grid Cell Realignment Following Nonmetric Context Change
to be compared. The phase shift (i.e., the distance translocated by
grid fields) was then calculated as the distance (in bins) from the
center of the peak closest to the center of the cross-correlogram.
Ensemble Analyses
To derive an estimate of whether cells reacted together, or as individuals, to a given change, we performed ensemble analyses on
sets of simultaneously recorded cells. A given value, being either
ratemap correlation or phase shift, was generated for each cell for
a given trial pair, and the resulting values then correlated between that cell and every other cell in the ensemble. The resulting set of numbers was then subjected to a Pearson’s r analysis.
Histology
Results
In total, 124 spatially firing neurons were recorded; the selection
criteria (see Materials and Methods) collectively resulted in exclusion of 26 neurons, leaving 98, and produced a dataset that
looked visually to include grid cells and not to include nongrid
cells. Histology showed that the grid cells were located in parasubiculum and MEC (see Supplementary Figs 1–4); an example of a
screening trial from each of the rats is shown in Supplementary
Figure 5, confirming that the cells from these regions were indeed
grid cells. Of the 98 cells in the final dataset, 71 were from superficial layers, 26 were from deeper layers, and 2 could not be classified. There were no apparent differences between the responses
of grid cells from the different layers and so all cells were
analyzed together.
Response of Grid Cells to Context Change
We first looked to see whether grid cells changed their firing rates
in response to context change, by comparing same-context and
different-context trial pairs and testing for “rate remapping”
(Leutgeb et al. 2005). Rate remapping was computed for each
cell as the unsigned difference in rates divided by their sum; a
paired t-test comparison of these values for the same-context
and different-context trials was conducted for both peak rates
and mean rates. The peak firing rate change was 0.15 Hz for the
same-context comparison and 0.16 Hz for the different-context
comparisons, and did not differ (t (97) = 0.67, NS). The mean firing
rate change was 0.16 and 0.14 Hz for the same-context comparison and different-context comparisons, respectively, which
likewise did not differ (t (97) = −1.20, NS).
In contrast, grid cells showed a distinct change in the position
( phase) of their grids in response to context change (Fig. 2b). This
occurred in response to both color and odor, although it was more
pronounced in response to odor (see below). The phase shift was
confirmed by correlation of the firing rate maps, which showed
high correlation for the within-trial comparison between first
and second trial-halves (a check for stability) and also between
| 5
same-context trials, but showed decreased correlation—realignment—after context change (Fig. 2c,d). A one-way ANOVA found a
main effect of comparison type (i.e., within-trial, same-context,
color-change, or odor-change; F3,97 = 60.00, P < 0.0001), with correlations being significantly different between same-context and
color-change conditions (same-context correlations = 0.50 ± 0.2,
color-change correlations = 0.37 ± 0.2; t (97) = 4.42, P < 0.0001,
and same-context and odor-change conditions (odor-change
correlations = 0.15 ± 0.2; t (97) = 9.31, P < 0.0001). Propensity to
realign was greater following odor change than color change
(t (97) = 6.25, P < 0.0001). This was confirmed when realignment
was assessed categorically (correlation threshold of 0.35; see
Fig. 2c); by this criterion, of the 98 cells, 77 responded to odor
and 45 to color, which was significantly fewer (χ 2(1, N = 98) =
22.23, P < 0.0001). Thus, grid cells responded to context changes
and were more sensitive to odor change than to color change;
indeed, only 8 of these cells responded to color changes alone,
while 40 responded to odor changes alone (37 responded to
both). Thirty-nine (40%) of the grid cells responded “conditionally”
(i.e., response to color change depended on which odor was present, and vice versa; e.g., Fig. 3b), indicating convergence of these
modalities outside of the cells themselves (Jeffery et al. 2004).
A subset of grid cells (n = 33) had been recorded in the larger
boxes, enabling analysis of metric grid properties of grid scale,
orientation, and phase shift from those trials (see Supplementary
Methods). The main contextual changes were due to phase shift.
We did not find significant changes in grid scale or orientation;
grid scale comparison (Fig. 4a) between same-cell context pairs
and different-cell context pairs (one-way ANOVA on the difference
scores) found a significant effect of comparison type (same-cell
versus different-cell; F2,32 = 42.6, P < 0.0001) with same-context
and different-context pairs not different (t(32) = 0.36, NS), but both
differing from the shuffled set (same-context versus shuffled; t(32)
= 6.43, P < 0.0001; different-context versus shuffled; t(32) = 8.32, P <
0.0001). Orientation also did not change (Fig. 4a); ANOVA showed a
main effect of comparison type [F2,32 = 7.08, P < 0.01] with both the
same-context and different-context changes in orientation being
small (9° and 11°, respectively) and not different (t (32) = 0.95, NS),
while both differed significantly from the shuffled set (18°; samecontext t(32) = 3.16, P < 0.01; different-context t(32) = 2.85, P < 0.01).
Ensemble behavior was examined to see if pairs of simultaneously recorded cells showed similar changes when compared
across pairs of contexts, consistent with their presumed attractor
dynamics (McNaughton et al. 2006). Twenty-seven ensembles
[sizes were 2 (n = 17), 3 (n = 5), 4 (n = 2), 5 (n = 3)] yielded a total of
444 cell-pair × trial-pair comparisons, for which ratemap correlations were high within ensembles (R = 0.73, P < 0.0001). The
amount of grid phase shift, obtained from the large-box dataset,
was also similar for simultaneously recorded cells (R = 0.46 and
0.47 for x and y, respectively; P < 0.0001; Fig. 4b).
Discussion
This study aimed to determine whether grid cells, an important
part of the brain network that computes spatial location, respond
only to metric spatial information or could also respond to purely
nonmetric “contextual” cues manipulated via changes in the
color and/or odor of the environment. The same-context paradigm has previously been reported to induce hippocampal
place cell remapping (Anderson and Jeffery 2003), and a possible
route of this remapping information is via the grid cell network.
In the present study, we found a substantial influence of context
(especially olfactory context) on the location of cell firing, resulting in grid cells translating their firing fields. It thus appears that
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At the end of the experiment, rats were deeply anesthetized and
perfused with saline followed by a 4% paraformaldehyde solution
(PFA), and the brains removed and stored in a solution of 4% PFA
and 20% sucrose prior to sectioning. Hemispheres that had received the entorhinal implant were cut sagittally at 40 μm intervals. Slices were mounted on gelatin-coated slides and stained
with cresyl violet. The depth and cell layer were ascertained by
pairing visual information from the microscope with estimates
of the distance that the electrodes were moved during recording.
Marozzi et al.
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ratemaps and autocorrelograms) responding coherently to odor change. (b) Two grid cells showing conditional responding (responding to color change only in vanilla
context). (c) Within-ensemble grid cell pairs responded similarly between context pairs.
grid cells receive nonmetric as well as spatial information, and
this information arrives via a different route from the information concerning direction. These findings add to the growing evidence that metric and nonmetric signals remain separated in the
spatial navigation system until a late stage in the processing
pathway, and suggest that the interaction occurs in the
entorhinal–hippocampal network. Interestingly, grid field orientation remained unaffected, and so the manipulation only affected those factors that control grid lateral positioning, and did
not affect those that orient it.
As well as examining grid cells in isolation, we examined the
ensemble behavior of the cells in order to see whether co-recorded
cells showed similar changes, consistent with grid cells being part
of an attractor network (McNaughton et al. 2006). Overall grid cells
showed that they responded to context in a homogeneous manner
(Fig. 3), which differs from the heterogeneous response of simultaneously recorded hippocampal place cells (Anderson and Jeffery
2003). Since we only recorded a few co-localized cells at a time, we
cannot rule out that anatomically separated cell assemblies in the
entorhinal cortex (so-called modules, which have strong intermodule coupling but weak intramodule coupling; Stensola et al. 2012)
respond independently to context change. Thus, the observation
of homogeneous realignment in grid cells is preliminary at present. However, it is worth noting that a recent extensive, detailed
analysis of grid cell realignment under environmental manipulation found no evidence of inhomogeneous responding (Yoon et al.
2013).
The motivation for the present study was to try to understand
better the way in which nonmetric information reaches the place
cell system—is it transmitted via the grid cells, or does it arrive
independently? The underlying hypotheses are laid out in
Figure 1. A previous study by Fyhn et al. (2007) supported the
hypothesis in Figure 1a—it found that metric environmental
change (induced by changing the room or altering the environment from a square to a circle) resulted in entorhinal grid translation and rotation together with complete hippocampal place
cell remapping, whereas nonmetric changes (induced by altering
the color of environmental walls) caused rate remapping in place
cells and no translation/rotation of the grid fields. This finding is
consistent with the hypothesis that grid cells transmit context
information to place cells via responding (Fig. 1a), but did not
rule out the possibility that the apparent context sensitivity of
grid cells might have been due to their detection of “metric”
changes to the environment, leaving open the hypothesis in
Figure 1b that grids cells are insensitive to purely nonmetric context, and that such signals impinge directly on place cells. Our
study shows that grids indeed shift (although they do not rotate)
in response to purely nonmetric change and rules out that
hypothesis.
Because place cells typically respond heterogeneously to our
context paradigm (Anderson and Jeffery 2003), whereas grid cells
responded coherently, grid cells look as though they may represent a final decision about context, perhaps received from the
place cells, rather than being an intermediate processing stage.
However, given the possibility discussed above that different grid
cell modules might realign independently, it is possible that anatomically neighboring place cells receive inputs from different grid
modules and can thus respond heterogeneously to changes in
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Figure 3. Homogeneity of grid cell responding (a) and (b) show ratemaps, depicted as in Figure 2. (a) Simultaneously recorded grid cells (confirmed by the screening trial
Grid Cell Realignment Following Nonmetric Context Change
Marozzi et al.
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Figure 4. Metric properties of grids (a) Top: scale and orientation were measured from the autocorrelograms derived from the ratemaps (scale = orange lines;
orientation = blue lines). Bottom: neither grid scale nor orientation differed for same- or different-context comparisons, whereas both differed from random
comparisons with shuffled data. (b) Left: grid shift was derived from the shift in the central peak of the cross-correlogram in X (orange dotted line) and Y (blue solid
line) from the center (black cross) between context pairs. Right: within-ensemble grid pairs shifted by similar amounts in the X and Y directions.
context. Our findings are thus not inconsistent with the notion
that the context signal to place cells is routed through the grid
cell network. The reverse is also a possibility however: that is,
that context modulates place cells directly and the place cells in
turn anchor grid firing fields via back-projections from the entorhinal cortex to the hippocampus (O’Keefe and Burgess 2005; Bonnevie et al. 2013) (Fig. 1c). This would be consistent with recent
findings that inactivating the hippocampus disrupts grid cell firing
(Bonnevie et al. 2013), and that manipulations that heavily attenuate theta band oscillations in the MEC and simultaneously abolish
grid field firing do not affect the ability of the hippocampal place
cell system to establish and maintain representations in novel environments (Brandon et al. 2014). Further work is needed to establish the direction of contextual information flow.
Stepping back from the grid and place cells, the question
arises as to how context information reaches this network from
outside. Several investigators have suggested that nonspatial information might be routed via the lateral entorhinal cortex (LEC):
for example, Hargreaves et al. (2005) showed that the LEC transmits far less spatial information than does the MEC, suggesting
that the LEC might be the pathway for incoming nonspatial information, with convergence occurring in the entorhinal cortex.
More recently, Lu et al. (2013) found that responsiveness of CA3
neurons in the hippocampus to color changes in the environment was reduced by LEC lesions. Areas of neocortex surrounding the EC also transmit information about objects, particularly
the perirhinal cortex (Deshmukh et al. 2012) and the postrhinal
cortex (Furtak et al. 2012), and may be part of a general system
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contextual remapping in place cells, remains to be determined,
and indeed will depend on what function the grid cells turn out
to actually have. One possibility is that their function is to support place cells in the temporary absence of external sensory information (e.g., in the dark, or perhaps even simply when
attention is diverted), and that under such conditions, the context modulation of grid cells helps them appropriately drive the
place cells.
Supplementary Material
Supplementary material can be found at: http://www.cercor.
oxfordjournals.org.
Funding
proposal, entorhinal grid cells position their grids using combined contextual
and spatial inputs and orient the grids using directional information.
Contextual inputs also project directly into place cells, which compute a final
decision about context which they feed back onto the grid cells.
Notes
whose role is to assemble incoming place-related information to
be combined with metric information about distance/direction
from borders.
How does the context signal interact with the spatial one to
position grids? Some investigators have suggested that the relevant spatial cues are the local boundaries of the environment
which could play a role in anchoring the grid pattern (O’Keefe
and Burgess 2005; McNaughton et al. 2006; Barry et al. 2007), perhaps acting via border cells or boundary vector cells (Savelli et al.
2008; Solstad et al. 2008). Two recent studies have highlighted the
ongoing role of boundaries in constraining grid positioning
(Krupic et al. 2015; Stensola et al. 2015). However, border cells
are context-insensitive (Solstad et al. 2008; Lever et al. 2009); so
why do grid cells shift their grids when the context changes?
Our observation suggests that either border cells are not the signal, or else they are not the only signal that anchors grid cells to
the environment. It may be that context functions to gate the
interaction between border cells and grid cells, in a manner
akin to the one we have previously proposed for the interaction
of contextual and spatial inputs to place cells (Hayman and Jeffery 2008): that is, the context inputs select which of the spatial inputs (mediated via, for example, border cells) gain control of the
grid and place cells. Alternatively, perhaps border cells drive
place cells directly, and the sensitivity of grid cells to particular
border/context combinations derives from the feedback projection from place cells. Untangling the complex causal relationships in this network will ultimately require the application of
interventional methods such as optogenetics.
Putting all this together, our emerging hypothesis is the one
illustrated in Figure 5: that nonmetric context information impinges directly onto both grid cells and place cells, but that the
grid cell and place cell networks also interact. The final outcome
is one in which a complex population code for spatial context is
sent out from the place cells, while possibly (with the caveat
about modules discussed above) a simpler, homogeneous code
is used by the grid cells to position their grids. Whether this positioning has functional relevance for the computing of spatial
context, or whether it is an uninformative side effect of
Conflict of Interest: None declared.
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The work was supported by studentships from the Biotechnology
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